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Scene labeling consists of labeling each pixel in an image with the category of the object it belongs to. We propose a method that uses a multiscale convolutional network trained from raw pixels to extract dense feature vectors that encode regions of multiple sizes centered on each pixel. The method alleviates the need for engineered features, and produces(More)
This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the(More)
— Intelligent tasks, such as visual perception, auditory perception, and language understanding require the construction of good internal representations of the world (or " features "), which must be invariant to irrelevant variations of the input while, preserving relevant information. A major question for Machine Learning is how to learn such good(More)
In this paper we present a scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms—neuFlow—and a dataflow compiler—luaFlow—that transforms high-level flow-graph representations of these algorithms into machine code for neuFlow. This system was designed with the goal of providing real-time detection,(More)
— In this paper we present a scalable hardware architecture to implement large-scale convolutional neural networks and state-of-the-art multi-layered artificial vision systems. This system is fully digital and is a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images. We present a(More)
Convolutional Networks (ConvNets) are biologically-inspired hierarchical architectures that can be trained to perform a variety of detection, recognition and segmentation tasks. ConvNets have a feed-forward architecture consisting of multiple linear convolution filters interspersed with point-wise non-linear squashing functions. This paper presents an(More)
Many recent visual recognition systems can be seen as being composed of multiple layers of convolutional filter banks, interspersed with various types of non-linearities. This includes Convolutional Networks, HMAX-type archi-tectures, as well as systems based on dense SIFT features or Histogram of Gradients. This paper describes a highly-compact and low(More)
Most scene segmentation and categorization architectures for the extraction of features in images and patches make exhaustive use of 2D convolution operations for template matching, template search, and denoising. Convolutional Neural Networks (ConvNets) are one example of such architectures that can implement general-purpose bio-inspired vision systems. In(More)